Business Intelligence (BI) generally refers to software tools used to improve business enterprise decision-making. These tools are commonly applied to financial, human resource, marketing, sales, customer and supplier analyses. More specifically, these tools can include: reporting and analysis tools to present information, content delivery infrastructure systems for delivery and management of reports and analytics, data warehousing systems for cleansing and consolidating information from disparate sources, and data management systems to collect, store, and manage raw data.
OLAP tools are a subset of business intelligence tools. There are a number of commercially available OLAP tools including Business Objects OLAP Intelligence™ which is available from Business Objects Americas of San Jose, Calif. OLAP tools are a report generation tool, and a tool suited to ad hoc analyses. OLAP generally refers to a technique of providing fast analysis of shared multi-dimensional information stored in a database. OLAP systems provide a multi-dimensional conceptual view of data, including full support for hierarchies and multiple hierarchies. This framework is used because it is a logical way to analyze businesses and organizations. In some OLAP tools the data is arranged in a schema which simulates a multidimensional schema. The multi-dimensional schema means redundant information is stored, but it allows for users to initiate queries without the need to know how the data is organized.
The querying process for OLAP can involve, depending on the specific implementation, writing and executing a query. Multidimensional Expressions (MDX) is a query language for OLAP databases, like SQL is a query language for relational databases. Thus, an MDX statement can be used to query for a result from an OLAP data source, i.e., a cube. The MDX statement can resemble SQL statements where one can ask for data on a row and columns from a cube. As with an SQL query, each MDX query requires a data request (the “SELECT” clause), a starting point (the “FROM” clause), and a filter (the “WHERE” clause). These and other keywords provide the tools used to extract specific portions of data from a cube for analysis, e.g., to extract a slice.
An MDX query specifies the appropriate columns and rows within the SELECT clause (the “ON COLUMNS” and “ON ROWS” clauses). For example, the MDX query can have the following format:
SELECTAxis Dimensions Members  ON COLUMNS,Axis Dimensions Members  ON ROWSFROMCubeWHERESliced Dimensions MembersThe “Axis Dimensions Members” specify which columns and rows to select. The “Sliced Dimensions Members” specify which slice of the cube to select.
Due to the nature of OLAP data sources, there is a lot of redundant data and a lot of columns and rows are selected in queries of even moderate complexity. In addition, users typically include many members in their queries. These factors lead to some MDX queries that are too long to be successfully executed by an OLAP server. Part of a multidimensional application, or an interface to a multidimensional data source, that communicates with the multidimensional data source maintains a sets of members that will be queried. This set is enumerable. When data associated with the list of members is needed a multidimensional query, such as an MDX statement, is constructed. Previously this query included an enumeration over a list. For example, a fragment of the MDX query could be:
SELECT {member1, member2, . . . member45312, . . . } ON COLUMNS
For a large list of members, the multidimensional query could be a very large statement. With some multidimensional data sources there can be a hard limit on the size of queries.
Therefore, it would be desirable to provide a new technique for reducing the size of queries to multidimensional data sources.